IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Data Processing Languages for Business Intelligence. SQL vs. R

Listed author(s):
  • Marin FOTACHE


Registered author(s):

    As data centric approach, Business Intelligence (BI) deals with the storage, integration, processing, exploration and analysis of information gathered from multiple sources in various formats and volumes. BI systems are generally synonymous to costly, complex platforms that require vast organizational resources. But there is also an-other face of BI, that of a pool of data sources, applications, services developed at different times using different technologies. This is “democratic†BI or, in some cases, “fragmented†, “patched†(or “chaotic†) BI. Fragmentation creates not only integration problems, but also supports BI agility as new modules can be quickly developed. Among various languages and tools that cover large extents of BI activities, SQL and R are instrumental for both BI platform developers and BI users. SQL and R address both monolithic and democratic BI. This paper compares essential data processing features of two languages, identifying similarities and differences among them and also their strengths and limits.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL:
    Download Restriction: no

    Article provided by Academy of Economic Studies - Bucharest, Romania in its journal Informatica Economica.

    Volume (Year): 20 (2016)
    Issue (Month): 1 ()
    Pages: 48-61

    in new window

    Handle: RePEc:aes:infoec:v:20:y:2016:i:1:p:48-61
    Contact details of provider: Postal:

    Phone: 0040-01-2112650
    Fax: 0040-01-3129549
    Web page:

    More information through EDIRC

    References listed on IDEAS
    Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

    in new window

    1. Kane, Michael & Emerson, John W. & Weston, Stephen, 2013. "Scalable Strategies for Computing with Massive Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i14).
    2. Octavian DOSPINESCU & Marian PERCA, 2013. "Web Services in Mobile Applications," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(2), pages 17-26.
    Full references (including those not matched with items on IDEAS)

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:aes:infoec:v:20:y:2016:i:1:p:48-61. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Paul Pocatilu)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.